Integrated Environmental Assessment and Management — Volume 00, Number 00—pp. 1–15

Received: 2 December 2020 | Returned for Revision: 8 March 2021 | Accepted: 13 June 2021 1

Life Cycle & Sustainability

Introducing ground cover management in pesticide emissionmodelingCéline Gentil‐Sergent,1,2 Claudine Basset‐Mens,1,3 Christel Renaud‐Gentié,4 Charles Mottes,1,2

Carlos Melero,5 Arthur Launay,1,2 and Peter Fantke51CIRAD, HortSys, University of Montpellier, Montpellier, France2CIRAD, UPR HortSys, ELSA, Le Lamentin, Martinique, France3CIRAD, UPR HortSys, ELSA, Montpellier, France4USC GRAPPE, ESA‐INRAe, Angers, France5Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University ofDenmark, Produktionstorvet Kongens Lyngby, Denmark

AbstractGround cover management (GCM) is an important agricultural practice used to reduce weed growth, erosion and runoff,

and improve soil fertility. In the present study, an approach to account for GCM is proposed in the modeling of pesticideemissions to evaluate the environmental sustainability of agricultural practices. As a starting point, we include a cover cropcompartment in the mass balance of calculating initial (within minutes after application) and secondary (including additionalprocesses) pesticide emission fractions. The following parameters were considered: (i) cover crop occupation between therows of main field crops, (ii) cover crop canopy density, and (iii) cover crop family. Two modalities of cover crop occupationand cover crop canopy density were tested for two crop growth stages, using scenarios without cover crops as control. Fromthat, emission fractions and related ecotoxicity impacts were estimated for pesticides applied to tomato production inMartinique (French West Indies) and to grapevine cultivation in the Loire Valley (France). Our results demonstrate that, onaverage, the presence of a cover crop reduced the pesticide emission fraction reaching field soil by a factor of 3 comparedwith bare soil, independently of field crop and its growth stage, and cover crop occupation and density. When consideringcover exported from the field, ecotoxicity impacts were reduced by approximately 65% and 90%, compared with bare soil forgrapevine and tomato, respectively, regardless of the emission distribution used. Because additional processes may influ-ence emission distributions under GCM, such as runoff, leaching, or preferential flow, further research is required to in-corporate these processes consistently in our proposed GCM approach. Considering GCM in pesticide emission modelinghighlights the potential of soil cover to reduce pesticide emissions to field soil and related freshwater ecotoxicity. Fur-thermore, the consideration of GCM as common farming practice allows the modeling of pesticide emissions in inter-cropping systems. Integr Environ Assess Manag 2021;00:1–15. © 2021 The Authors. Integrated Environmental Assessmentand Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC)

KEYWORDS: Active ingredient, Cover crop, Environmental modeling, Farming practices, Life cycle assessment

INTRODUCTIONA transition to more agro‐ecological cropping systems is

urgently needed to ensure sustainable food productionsystems and consequently sustainable food security, based,

for example, on biological pest control, reduced tillage,intercropping, cover crops, or agroforestry (Tscharntkeet al., 2012; Wezel et al., 2014). This corresponds to theUnited Nations Sustainable Development Goal 2 (i.e., endhunger, achieve food security and improved nutrition, andpromote sustainable agriculture; Griggs et al., 2013;HLPE, 2019). One key practice in agro‐ecological croppingsystems is ground cover management (GCM). It is definedas the field soil occupation between crop rows and underthe crop canopy (Shaxson, 1999). Ground cover manage-ment can be provided by several farming practices, usingeither living cover (i.e., spontaneous or planted cover crops)or dead cover (e.g., mulch composed of crop residue andresidue from the previous crop, or impervious mulch such asplastic mulch; Mottes et al., 2014).

Integr Environ Assess Manag 2021:1–15 © 2021 The AuthorsDOI: 10.1002/ieam.4482

Correspondence Peter Fantke, Quantitative Sustainability Assessment,Department of Technology, Management and Economics, TechnicalUniversity of Denmark, 2800 Kongens Lyngby, Denmark.Email: pefan@dtu.dk

Published 23 June 2021 on wileyonlinelibrary.com/journal/ieam.

This is an open access article under the terms of the Creative CommonsAttribution‐NonCommercial License, which permits use, distributionand reproduction in any medium, provided the original work is properlycited and is not used for commercial purposes.

This article contains online‐only Supporting Information.

These ground covers (GC) are used to provide severalagricultural and environmental benefits. They limit weedsand consequently herbicide applications, and they improvesoil fertility and bearing capacity (CIRAD, 2009; Wezelet al., 2014). They also reduce erosion (Durán Zuazo &Rodríguez Pleguezuelo, 2008) and runoff as well as pesticide(i.e., the active ingredient in a pesticide formulation, here-after referred to as pesticide) transfer to surface water(Alletto et al., 2010; Mottes et al., 2014; Reichenbergeret al., 2007). Among these several benefits, the reduction inpesticide applications and transfers is particularly importantin tropical conditions, where the use of pesticides occurs allyear round, owing to high pest pressures and year‐roundcrop growth (Lewis et al., 2016; Mottes et al., 2017). GCpractices exist in all cultivated areas, in temperate andtropical conditions. For example in 2011, 49% of Frenchvineyards were cover cropped (Ambiaud, 2012), with forexample, spontaneous vegetation, oats, clover, and fescue(Renaud‐Gentié et al., 2015). In the tropical conditionsof Martinique, French West Indies, 23.2% of bananafarmers were using cover crops in 2015 (DAAF Marti-nique, 2018), composed of diverse species mixed or sepa-rated, for example Brachiaria decumbens, Stylosanthesguianensis (Tixier et al., 2011). For open‐field tomato pro-duction in Martinique, GCM practices are also common butnot formally recorded, and field studies are needed tobetter understand these practices. In support of addressingthis need, we selected tomato as the example crop in ourcase study.Life cycle assessment (LCA), an ISO‐standardized meth-

odology to evaluate the environmental performance ofproduct systems, is widely applied to agri‐food systems(Andersson, 2000; Poore & Nemecek, 2018; Roy et al., 2009)to quantify the environmental performance of farmingmanagement practices (Bessou et al., 2013; Renaud‐Gentiéet al., 2020; Rouault et al., 2016). In the context of agro‐ecological transition, there is an urgent need to addressthese practices, such as GCM, in LCA studies to comparedifferent farming systems, such as conventional, integrated,and organic farming (Meier et al., 2015) as well as to betterinform eco‐design (Rouault et al., 2020). As a prerequisite toconsidering GCM in LCA, pesticide emission distributionsneed to be modeled properly. However, whereas many LCAstudies do not yet consider pesticide emissions and relatedimpacts (Perrin et al., 2014), other approaches assume that100% of pesticides applied are emitted to agricultural soil(e.g., Nemecek & Schnetzer, 2011). More detailed ap-proaches exist, such as a generic distribution of pesticidesapplied between air and soil (e.g., Neto et al., 2013;Oliquino‐Abasolo, 2015) or modeling pesticides emissionsbased on a consistent mass balance, as in the PestLCI model(Birkved & Hauschild, 2006; Dijkman et al., 2012) and itssubsequent PestLCI Consensus version (Fantke et al., 2017).However, none of these LCA approaches currently considersGCM practices. Renaud‐Gentié et al. (2015) developed aninitial approach to considering the effect of living GCM forthe cover crops between vine rows by replacing the single

crop intercepted fraction by a combination of vine andcover crop interceptions according to their respective de-velopment stages. However, this first accounting of a covercrop for grapevine has never been extended to other typesof GCM or crops. Therefore, the current influence of GCMon the environmental performance of different croppingsystems can not be properly evaluated. To fill this gap, GCMneeds to be integrated into state‐of‐the‐art pesticide emis-sion models.

To extend the modeling of GCM to other types of GC andcrops in pesticide emission modeling, the definitions of thecover crop occupation and its canopy development werefirst proposed. The present study considers GCM composedof spontaneous or planted cover crops without being a cashcrop. The fraction intercepted by the cover leaves wasestimated and processes occurring on those leaves weresimulated (volatilization, degradation, and uptake), consid-ering as far as possible the cover family (e.g., Fabaceae).Furthermore, pesticide plant uptake, modeled by the cropexposure model dynamiCROP (Fantke et al., 2011), wasrecently coupled with the initial distribution of PestLCIConsensus (Gentil et al., 2020a). Consequently, the pesti-cide intercepted fraction by the crop has been distinguishedfrom that intercepted by the cover and other living plantsnot harvested in the field (e.g., buffer zone).

The main objective of the present study is to introduceGCM in pesticide emission modeling. To achieve this goal,three specific objectives were defined: (i) to propose anapproach to considering GCM in a state‐of‐the‐art pesticideemission model, (ii) to analyze the effect of GCM ondistributions of emissions and related freshwater ecotoxicityimpacts, and (iii) to test our approach in a case study usingdifferent GCM types under temperate and tropical climateconditions.

MATERIALS AND METHODS

Ground cover management modeling

The GCM was introduced in PestLCI Consensus, a massbalance model that calculates fractions of pesticide massreaching different plant‐environment compartments (in‐fieldcrop leaves, in‐field soil, in‐ and off‐field air, off‐field sur-faces, and in‐field groundwater) with two distributions(Birkved & Hauschild, 2006; Dijkman et al., 2012). The initial(or primary) distribution of PestLCI considers processes oc-curring a few minutes after pesticide application, with apesticide distribution to air, off‐field surfaces, field soil, andfield crop. Initial distribution is followed by the secondaryemission processes occurring over a default duration of 24 h(Fantke et al., 2017).

In the following, we present the methodological frame-work for integrating GCM into the PestLCI Consensusemission model, which is then applied in an illustrated casestudy. To integrate GCM in PestLCI Consensus, a newcompartment in the model for the cover crop was in-troduced, in addition to the four existing initial distributioncompartments (i.e., air, off‐field surfaces, field soil, and field

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2 Integr Environ Assess Manag 00, 2021—GENTIL‐SERGENT ET AL.

crop). All five compartments are used in the modelinganalysis of the present study. Data required to define thecover crop are (i) cover crop occupation fraction betweenthe rows and the stems of the main crop, below the cropcanopy (i.e., area fraction of crop‐free field soil that is oc-cupied by cover crop), fsoil cover→ ; (ii) cover crop canopyfraction (i.e., area fraction of cover crop that is covered byleaves), fintercept,cover (Renaud‐Gentié et al., 2015); and (iii)cover family (e.g., grass composed of Pooideae). The covercrop occupation fraction, fsoil cover→ , depends on the farmer'sobjectives for applying GCM and also depends on the crop,its growth, and field characteristics (e.g., slope, crop family,soil; Abad et al., 2021; Renaud‐Gentié et al., 2015). Thefraction intercepted by the cover, fintercept,cover, is a functionof the leaf area index (LAI) defined as the ratio of the leafsurface area (one side) per unit of ground surface area(m² m−²; Watson, 1947), and it is derived analogously to thefraction intercepted by field crop leaves (fintercept,crop). In thecurrent PestLCI Consensus model, they were defined basedon Linders et al. (2000), using the growth stages with theBBCH‐scale (Meier, 2018).For this purpose, the PestLCI model web tool, in a beta

version, was used, into which we implemented the equa-tions for GCM. The following sections describe a full ap-proach to consider GCM in PestLCI, based on adaptingseveral processes and the mass balance in the model.

Initial distribution fractions

Using the initial PestLCI mass balance (Dijkmanet al., 2012) as a starting point, a few minutes after pesticide

application, a fraction of pesticides is deposited to off‐fieldsurfaces (fdep). It is derived from drift deposition functionsspecific to each application method. Another fraction goesto the air by wind drift (fair) as a default fraction per appli-cation method and crop, and a last fraction goes to thefield (ffield):

f f f1 field air dep= + + (1)

Figure 1 presents the pesticide distribution to the variouscompartments.After re‐arranging Equation (1), the fraction reaching the

field surface area (ffield) was obtained:

f f f f f1field air dep field crop field rest= − ( + ) = +→ → (2)

The fraction reaching the field surface area (ffield) is partiallydeposited on crop leaves (ffield crop→ ) according to crop in-tercepted fraction fintercept,crop and calculated as:

f f ffield crop field intercept,crop= ×→ (3)

Then, the fraction left on the field after crop interception(ffield rest)→ is calculated as:

f f f f f1field rest field intercept,crop field cover field soil= × ( − ) = +→ → →

(4)

The newly introduced fractions of pesticides reaching thecover leaves (ffield cover)→ and the field soil ffield soil( )→ are thencalculated according to the effective area fraction of crop‐free field that is covered by cover crop (feff,cover):

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FIGURE 1 Illustration of initial pesticide distribution fractions to air (fair ), off‐field surfaces (fdep), field crop leaves (ffield crop→ ), cover crop leaves (ffield cover→ ), andfield soil surface (ffield soil→ )

GROUND COVER IN PESTICIDE EMISSION MODELING—Integr Environ Assess Manag 00, 2021 3

f f ffield cover field rest eff,cover= ×→ → (5)

f f f1field soil field rest eff,cover= × ( − )→ → (6)

where feff,cover is calculated by multiplying the area fractionof crop‐free field that is cover crop (fsoil cover→ ) and the areafraction of cover crop that is covered by leaves (fintercept,cover):

f f feff,cover soil cover intercept,cover= ×→ (7)

Secondary emission fractions

Again building on the initial PestLCI mass balance(Dijkman et al., 2012) from the initial distribution fractions,secondary emissions processes occur. All processes definedin PestLCI for the main crop leaves were also applied tocover crop leaves, that is, degradation, uptake, and volati-lization. The fractions degraded and volatilized were ag-gregated with the respective processes occurring on themain crop, ultimately affecting fractions emitted to air andfield soil. A new emission output fraction, fcover sec‐ , was cre-ated to consider the uptake fraction by the cover leaves andthe fraction remaining on cover leaves that was not yet takenup by the cover leaves.The degradation on cover crop leaves was integrated in

the total degraded fraction of pesticide. To consider thecover family (e.g., Pooideae), the degradation rate constantfor pesticides on cover leaves (kdegCover) in (d−1) was calcu-lated. Based on a database of measured dissipation half‐lives (Fantke & Juraske, 2013), the pesticide dissipation half‐life (d) on leaf cover at the average temperature in themonth of application (DT50 Tcover, ) was used as a proxy, and adissipation half‐life model was derived, using a temperaturecoefficient (Fantke et al., 2014) to correct for actual fieldtemperature. Thus, the following correction was applied toassess the dissipation half‐life on leaf cover at the averagetemperature in the month of application (DT50 Tcover, ) as:

DT50 10TT T

cover,log DT50 0.01995T10 cover, ref ref( )= ( )− × − (8)

where DT50 Tcover, ref is the pesticide half‐life on cover leaf atreference temperature (20 °C) (d), T the average temper-ature in the month of application (°C), and Tref the temper-ature at which the dissipation half‐life is reported (20 °C).DT50 Tcover, ref is selected from a list of coefficients for cropsthat correspond to one of the relevant cover families (e.g.,Pooideae).Then, analogous to the pesticide translocation and re-

moval processes implemented for the field crops, the deg-radation rate constant for pesticides on cover leaves ataverage temperature in the month of application (d−1) wascalculated as:

kln 2

DT50 TdegCover

cover,=

( )(9)

Finally, the overall rate constant for the cover leaves kOCover

(d−1) was calculated as:

k k k kOCover volat uptake degCover= + + (10)

where the rate constant for volatilization from the leaf sur-face, kvolat (d

−1), and the rate constant for leaf uptake, kuptake

(d−1), are both modeled independently of the crop or coverfamily, and where the degradation rate constant for pesti-cides on cover leaves, kdegCover (d

−1) is modeled accordingto Equation (9).The processes of degradation, volatilization, and uptake oncover leaves are calculated until the time defined for thesecondary emissions to occur after pesticide application(default: t 1assess = d).Considering the crop family of the cover, the degradationon cover leaves (fdegCover) is different from that on the maincrop, and was calculated as:

f fk

ke1 k t

degCover field coverdegCover

OCover

OCover assess= × × ( − )(− × )→

(11)

where ffield cover→ is the fraction reaching cover leaves afterpesticide application, Equation (5); kdegCover is the degra-dation rate constant for pesticides on leaf at average tem-perature in the month of application (d−1), Equation (9); andkOCover is the overall rate constant dissipation on coverleaves (d−1), Equation (10).

The volatilization on cover leaves (fvolCover) was calcu-lated as:

f fk

ke1 k t

volCover field covervolat

OCover

OCover assess= × × ( − )(− × )→

(12)

where ffield cover→ is the fraction reaching cover leavesafter pesticide application, Equation (5); kvolat is the rateconstant for volatilization from the leaf surface (d−1); andkOCover is the overall rate constant dissipation on cover (d−1),Equation (10).

The cover leaves uptake (fuptakeCover) was calculated as:

f fk

ke1 k t

uptakeCover field coveruptake

OCover

OCover assess= × × ( − )(− × )→

(13)

where ffield cover→ is the fraction reaching cover leaves afterpesticide application, Equation (5); kuptake is the rate con-stant for leaf uptake (d−1) independent of the crop or coverfamily; and kOCover is the overall rate constant dissipation oncover leaves, Equation (10).

The fraction remaining on cover leaves (fLeaves cover‐ ) thatwas not degraded, taken up, or volatilized at t 1 dassess = ,was calculated as:

f f e k tLeaves cover field cover OCover assess= ×‐

(− × )→ (14)

where ffield cover→ is the fraction reaching cover leavesafter pesticide application, Equation (5); and kOCover is theoverall rate constant for removal from cover leaves (d−1),Equation (10).

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4 Integr Environ Assess Manag 00, 2021—GENTIL‐SERGENT ET AL.

The total fraction in and on cover leaves (fcover sec)‐ was cal-culated by summing the fraction uptake from cover leaves(fuptakeCover) and the fraction remaining on cover leaves(fLeaves cover)‐ as:

f f fcover sec uptakeCover Leaves cover= +‐ ‐ (15)

Other processes not affected by the presence of a covercrop (e.g., processes related to crop leaves) are detailedelsewhere (Dijkman et al., 2012).

Propagation of GCM to impact score level

To evaluate how the introduction of GCM influences finalLCA results, impact scores (IS) for freshwater ecotoxicityimpacts of pesticide emissions were calculated with USEtox(Rosenbaum et al., 2008), considering the application of1 kg of pesticide on a tomato or grapevine field of 1 ha. Theimpact score (IS in PAF m3 d/kgapplied) was calculated as:

m CFISp c p c p c, emi, , ,= ( × )∑ (16)

where m p cemi, , (kgemitted/kgapplied) is the total emitted massof pesticide p from the crop production into a given envi-ronmental compartment c , and CFp c, (PAF m3 d/kgemitted) isthe corresponding characterization factor for freshwaterecotoxicity. Related impact characterization factors forfreshwater ecotoxicity of the two considered pesticides(mancozeb and pyriproxyfen) are presented in SupportingInformation, Table S2.As recommended in Fantke et al. (2018) and applied in

Gentil et al. (2020a), emissions to air were assigned in theimpact assessment model USEtox to continental rural air;emissions to field soil were assigned to continental agricul-tural soil; and emissions to groundwater were assigned tocontinental freshwater. Off‐field surface emissions were as-signed to continental agricultural soil, natural soil, andfreshwater according to the typical area share of eachcompartment in a given region. Cover uptake fractions,along with crop uptake fractions and degradation, were notlinked to the impact assessment when cover crop was con-sidered to be removed from the field. Because the fractionsof applied pesticides that are taken up into crops are sub-sequently harvested (field crops) or otherwise removed fromthe field (cover crops), these fractions are assumed not toreach the environment and hence do not contribute to im-pacts on ecosystems in our study. When cover crop was notremoved from the field (e.g., buried), then impacts frompesticides present on the cover were attributed to the ag-ricultural soil compartment. The fraction reaching the har-vested field crop is attributed to a separate exposurepathway contributing to human toxicity impacts (Fantkeet al., 2021).

Definition of illustrative case study underdifferent conditions

To evaluate the proposed GC modeling approach, pes-ticide applications were simulated for grapevine (Vitis

vinifera) in the Loire Valley in France (Figure 2A); followingthe initial work done on GCM in grapevine production in theLoire Valley by Renaud‐Gentié et al. (2015), and an open‐field tomato crop (Solanum lycopersicum) in the tropicalconditions in Martinique in French West Indies (Figure 2B),for which a previous PestLCI model version was tested(Gentil et al., 2020a). With that, different conditions of crop,climate, soil, application method, and cover crop wereconsidered (see Table 1). A default situation was defined forfield characteristics and farming practices to allow feasiblecomparisons between crop production systems (i.e., fieldlength and width of 100m, no slope, no drift reductionsystem on the application method, no drainage system, noirrigation, and no tillage). Initial emissions to air (fair) and tooff‐field surfaces (fdep) were defined according to pesticidedrift caused by the selected application method, that is, witha knapsack sprayer on tomato (García‐Santos et al., 2016)and with an air‐assisted sprayer side‐by‐side flat fan nozzleson grapevine (Codis et al., 2011). To estimate secondaryemission fractions, a default time assessed at 1 day wasconsidered.To assess the variability of pesticide distribution on cover

crop, different growth stages of the main crop (fintercept,crop)were considered, as well as the effective area fraction ofcrop‐free field that is cover cropped (feff,cover). For eachscenario, two main crop growth stages were considered,namely the leaf development and the flowering stage/de-velopment of fruit/ripening. They were defined asf 0.3intercept,crop= for leaf development and f 0.8intercept,crop =

for flowering (Linders et al., 2000), corresponding to thecrop growth stage of pesticide applications. Two types ofcover crop were considered, a planted cover crop on therow of the main crop, f 0.5soil cover =→ , with a high pesticideinterception fraction of f 0.7intercept,cover = (Renaud‐Gentiéet al., 2015), and a spontaneous cover crop covering all fieldsoil between the main crop with f 1soil cover =→ and a highpesticide interception fraction of f 0.7intercept,cover = . There-fore, the effective area fractions of cover crop aref 0.35eff,cover = and f 0.7,respectivelyeff,cover = . Each of thecover crops was composed of either grass from Pooideae orclover from the Fabaceae family for the grapevine pro-duction, and grass from Panicoideae or Stylosanthesguianensis from the Fabaceae family for the tomato pro-duction. Covers from Pooideae family are present mainly intemperate and dry climates, whereas covers from Pan-icoideae family dominate tropical and subtropical areas(Zuloaga et al., 2007). Cover crops of the Panicoideae andPooideae families are grass, either planted or spontaneous,and cover crops from the Fabaceae family are legumesplanted. The cover crop family is required to estimate thepesticide degradation on leaves (see Equation [8]). Gen-erally, pesticide dissipates from leaves faster than from soil(Juraske et al., 2008) and varies according to plant family(Fantke & Juraske, 2013). Two pesticides homologated andused on tomato and grapevine were tested with contrasteddissipation half‐lives on soil and plant, with two targetclasses, namely the insecticide pyriproxyfen and the

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GROUND COVER IN PESTICIDE EMISSION MODELING—Integr Environ Assess Manag 00, 2021 5

fungicide mancozeb. Because GCM is used partly to reducethe use of herbicides, which can also negatively affect theground cover crops themselves, no herbicides were in-cluded in our case study. Their relevant physicochemicalproperties are detailed in Table S1.Pesticide emissions were assigned in the impact assess-

ment model USEtox. The off‐field deposition fraction wasshared according to prevailing surface compartments in theregion of each scenario using the CORINE Land Cover data(data.gouv.fr). In Martinique, the off‐field deposition fractionwas allocated as follows: 29% to agricultural soil, 70% tonatural soil, and 1% to freshwater, and in the Loire Valley,67% to agricultural soil, 31.2% to natural soil, and 1.8% tofreshwater.

Based on the calculation approach to emission dis-tribution under GCM conditions, and the related calculationof ecotoxicity impacts in the case study, the effect of GCMon emission and related freshwater ecotoxicity impact re-sults was analyzed.

Analysis of GCM effect on pesticide emissionsand related impacts

To assess cover crop effects on initial and secondarydistributions, control scenarios without cover (i.e., with baresoil) for each combination of pesticide, crop, and inter-cepted fractions (i.e., eight control scenarios with feff,cover =0) were designed and simulated. Distribution fractions arepresented as a function of continuously increasing cover

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FIGURE 2 Cover crop planted on grapevine cultivation in Anjou, in the Loire Valley (A) and spontaneous cover on tomato production in Martinique, FrenchWest Indies (B)

6 Integr Environ Assess Manag 00, 2021—GENTIL‐SERGENT ET AL.

crop area fractions. After an analysis of the influence of theeffective area fraction of cover crop feff,cover( ) on initial dis-tribution fractions, a second analysis was carried out on thesecondary emission fractions and particularly on processesinvolved on cover leaves, namely degradation, volatilization,and uptake.At the impact level, scenarios with and without a cover

crop were compared by calculating the percentage ofchange of IS for freshwater ecotoxicity (PAF m3 d/kgapplied),for each considered environmental emission compartment.The percentage of change (% change− ) was calculated as:

% changeIS without cover IS with cover

IS without cover100− =

−×⎡

⎣⎤⎦

(17)

When pesticide emissions to cover leaves are assigned tothe agricultural soil compartment as field soil surface emis-sions, this corresponds for initial distribution fractions to ascenario without considering any cover. Therefore, the initialemission fraction reaching cover leaves, ffield cover→ , was notlinked to the impact assessment but considered as a re-moval process as for the pesticide emission on crop leaves.For secondary emissions, two agronomic situations wereconsidered for the fraction taken up by the cover and left oncover leaves (fcover sec)‐ . One situation considered that thecover crop was exported from the actual field crop, that is,mowed and transferred outside the field. Hence, fcover sec‐

was not linked to the impact assessment. The second sit-uation considered the cover buried in the field soil by, forexample, a tillage practice, so fcover sec‐ was assumed to reachagricultural soil as emission. In summary, three situationswere analyzed: (i) initial distribution with cover exportedfrom the field, (ii) secondary emissions with cover exportedfrom the field, and (iii) secondary emissions with coverburied in the field soil.

RESULTS

Emission results

Across a range of effective area fractions of cover (feff,cover)

for the tomato and grapevine scenarios, the variability ofinitial and secondary distribution fractions is presented inFigure 3. Emission fractions for the two cover crops(f 0.35eff,cover = and f 0.7eff,cover = ) are detailed in Table 2.At initial distribution, the air‐assisted sprayer in grapevine

displays higher off‐field surfaces emissions, f 0.04dep = andhigher emission to air, f 0.08air = , than the knapsack sprayerused in tomato production with f 0.02dep = and f 0.06air = .The differences of emission fractions to air and off‐fieldsurfaces are linked to the characteristics of the applicationmethod as discussed in Gentil‐Sergent et al. (2021). In thepresent case study, only agronomically relevant applicationmethods were tested for each considered crop productionsystem.The initial pesticide distributions to soil and cover crop

depend on the main crop intercepted fraction and the ef-fective area fraction of cover. The higher the fraction inter-cepted by the crop (reproductive‐flowering phase), thelower the fraction distributed under the crop canopy (i.e., tothe field soil and cover crop). Indeed, in our case study,when the main crop is starting to grow, the fraction ofpesticide distributed between the soil and the cover cropwas 3.5 times higher than when the main crop is at theflowering stage (Table 2). On average, the presence of acover crop reduced the fraction reaching field soil by afactor of 3 compared with a bare soil regardless of the fieldcrop, its growth stage, and the effective area fraction ofcover. The variation in the fraction reaching field soil amongscenarios was caused mainly by different effective areafractions of cover, driven by the area fraction of crop‐freefield that is cover crop and the area fraction of cover cropthat is covered by leaves corresponding to the cover leaves

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TABLE 1 Main characteristics of the case study and the two scenarios: grapevine and tomato production

Crop Grapevine Tomato in open‐field

Location Loire Valley (France) Martinique (French WestIndies)

Climate type andweather station

Marine West CoastClimate (Cfb)* ‐Beaucouzé weatherstation

Tropical savanna climate(Aw)* ‐ Le Prêcheur,Météo France weatherstation

Soil type Sand on calcareousformation

Vitric andosol (FAOsoil data)

Application method Air‐assisted sprayer side‐by‐side flat fan nozzles

Knapsack sprayer

Cover crop family Grass (Pooideae), Clover(Fabaceae)

Grass (Panicoideae),Stylosanthesguianensis (Fabaceae)

ffeeffff ccoovveerr, 0.35 and 0.7

*From Köppen–Geiger climate classification.

GROUND COVER IN PESTICIDE EMISSION MODELING—Integr Environ Assess Manag 00, 2021 7

density. The higher the effective area fraction of cover, thelower the fraction distributed to field soil. The effect of covercrop on pesticide initial distribution fractions to field soil andcover crop was propagated to the secondary emissionfractions (Figure 3).When including secondary emission distribution proc-

esses, cover crop leaf uptake contributed more than 99% tocover‐crop‐related processes, yielding an average leaf up-take fraction of 0.22 across all scenarios. The total degradedfractions (on soil, on‐field crop, and cover leaves) in our casestudy were highest when there was no cover crop. This isexplained by the fact that, despite faster degradation incrops and cover crops compared with soil, the even fasterplant uptake into both crop and cover crop drives theoverall pesticide distribution within 24 h that are consideredin the emission model. Consequently, most of the pesticide

is taken up into crop, while degradation processes will drivesubsequent fate processes that are modeled as part of theimpact assessment. The degraded fractions on cover leavesdisplayed only slight differences between the cover cropfamilies, that is, Pooideae vs. Fabaceae for grapevine sce-narios and Panicoideae vs. Fabaceae for tomato scenarios.Indeed, degradation on Fabaceae leaves was lower than onPooideae and Panicoideae, owing to longer dissipationhalf‐lives on Fabaceae (see detail of DT50 in Table S3). Onleaves, the only process considering crop and cover family isdegradation. The fraction degraded on cover leaves was, onaverage, less than 0.1% but could reach a maximum of0.65% for the mancozeb application with full coverage ofFabaceae (i.e., feff,cover = 1). The fraction taken up by thecover that depends on the initial distribution fraction oncover leaves rose to approximately 18% when the crop

Integr Environ Assess Manag 2021:1–15 © 2021 The Authorswileyonlinelibrary.com/journal/ieam

(A) Initial distribution fractions

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FIGURE 3 Initial (A) and secondary (B) emission distribution fractions for two crop growth stages, f 0.3intercept,crop = and f 0.8intercept,crop = , respectively,corresponding to the installation (a, c) and flowering stage (b, d), for grapevine (a, b) and tomato (c, d) for a range of effective area fractions of crop‐free fieldthat is covered by cover crop (feff,cover). Vertical lines represent the cover crop setup of the case study with f 0.35eff,cover = and f 0.7eff,cover =

8 Integr Environ Assess Manag 00, 2021—GENTIL‐SERGENT ET AL.

growth stage was on maturation (f 0.8intercept,crop = ), androse to approximately 60% when the crop growth stage wason installation (f 0.3intercept,crop = ) for both scenarios.The presence of a cover crop did not modify initial dis-

tribution fractions to air, crop, and off‐field surfaces. It didnot modify either secondary emission fractions to off‐fieldsurfaces, crop uptake, and fraction left on crop leaves. Forsecondary distribution scenarios, processes that are by de-fault initiated after the first day after application were notincluded in our study but require further analysis to ensureconsistency with our proposed GCM processes and toevaluate emissions eventually reaching groundwater orsurface water. With that, the fractions emitted to off‐fieldsurfaces did not vary with the presence or absence of acover crop in our case study.Overall, during initial and secondary distributions, the

introduction of a cover crop with its own leaf surface areareduced the fraction reaching the field soil.

Impact results

The IS for the scenarios with a cover crop and the per-centage of change compared with the scenarios without acover (i.e., bare soil) are presented in Table 3. The con-tribution of the USEtox environmental compartments to thetotal IS calculated for the scenarios with and without covercrop for tomato and grapevine are presented in Figure 4according to the distribution used (initial or secondary).Pesticide initial distribution fractions were assigned to the

environmental compartments of the impact assessmentmodel (i.e., USEtox), except the emission fractions to cropand cover leaves, because the plant compartment does not

exist in the model. Only the impacts caused by agriculturalsoil emissions were affected by the consideration of a covercrop, with a reduction of approximately 100% regardless ofcrop type. As a consequence, the IS using initial distributionfractions were reduced by 65% for grapevine and by 90% forthe tomato crop with the use of a cover crop.Two connections were assessed between secondary

emission fraction to cover and USEtox compartments. First,as for the connection with the initial distribution, no link toany USEtox compartment was assumed for the emissionfraction to cover, considering cover exported from the field.Second, the fraction taken up by and present on coverleaves was assigned to agricultural soil, considering thecover buried in the field soil.Using either initial or secondary distribution fractions, IS

caused by emissions to the natural soil were not affected bycover crops; they are related to emissions from off‐fieldsurfaces. In our two scenarios, the variation in IS to air withor without a cover was very small (% change 10 %5− = )− . Avariation of one order of magnitude was observed on the IScaused by emissions to air across scenarios caused by thevapor pressure of the two pesticides tested (see details ofpesticide characteristics in Table S1). IS for emissions tocontinental freshwater (including emissions to ground andsurface water) were not affected by the presence of a covercrop. In all scenarios, impact results based on secondaryemission fractions were caused mainly by emissions to thecontinental agricultural soil compartment (Figure 4).In all scenarios with and without cover crop, the total IS

were higher on grapevine than on tomato. This is mainly theresult of the higher emission fractions to off‐field surfaces for

Integr Environ Assess Manag 2021:1–15 © 2021 The AuthorsDOI: 10.1002/ieam.4482

TABLE 2 Initial and secondary emission fractions for the tomato and grapevine production, for two crop growth stages, f 0.3intercept,crop =

and f 0.8intercept,crop = , for the effective area fraction of crop‐free field that is covered by cover crop representing the two cover crop typeswith f 0.35eff,cover = and f 0.7eff,cover = (mean across scenarios)

Crop → Tomato Grapevine

fintercept,crop → 0.3 0.8 0.3 0.8

↓ Distribution feff,cover→Compartment ↓ 0.35 0.7 0.35 0.7 0.35 0.7 0.35 0.7

Initial Air 0.06 0.06 0.06 0.06 0.08 0.08 0.08 0.08

Off‐field surfaces 0.02 0.02 0.02 0.02 0.04 0.04 0.04 0.04

Crop leaves 0.28 0.28 0.74 0.74 0.27 0.27 0.71 0.71

Field soil 0.42 0.19 0.12 0.06 0.40 0.19 0.12 0.05

Cover leaves 0.23 0.45 0.07 0.13 0.22 0.44 0.06 0.12

Secondary Air 0.06 0.06 0.06 0.06 0.08 0.08 0.08 0.08

Off‐field surfaces 0.02 0.02 0.02 0.02 0.04 0.04 0.04 0.04

Groundwater 0 0 0 0 0 0 0 0

Field soil 0.35 0.16 0.10 0.05 0.37 0.17 0.11 0.05

Degradation 0.07 0.03 0.02 0.01 0.04 0.02 0.01 0.01

Crop uptake 0.28 0.28 0.74 0.74 0.27 0.27 0.71 0.71

Cover 0.23 0.45 0.07 0.13 0.22 0.43 0.06 0.12

GROUND COVER IN PESTICIDE EMISSION MODELING—Integr Environ Assess Manag 00, 2021 9

grapevine, with higher off‐field surfaces water area fractionin the Loire Valley (1.8%) than in Martinique (1%), and withhigher characterization factors (CF) on emissions directlyinto the freshwater compartment than into other compart-ments (detail of CFs in Table S2).Overall, across the two scenarios, the modeling of GCM

demonstrated the potential to reduce emissions to field soiland related freshwater ecotoxicity impacts. This is becauseecotoxicity impacts are driven mainly by pesticide fractionsdirectly reaching surface water via deposition on off‐fieldareas as well as via fractions depositing on field soil andsubsequently reaching water bodies. Moreover, when con-sidering the cover exported from the field, and con-sequently not assigned to any environmental compartmentin the impact assessment, total IS were reduced by ap-proximately 65% (grapevine) and 90% (tomato) comparedwith bare soil scenarios for initial and secondary emissions.Conversely, when considering the cover buried in the fieldsoil, total IS were similar to bare soil, reduced by only 1%(grapevine) and 3% (tomato).

DISCUSSION AND OUTLOOK

Applicability and limitations of the presented approach

The modeling of GCM in pesticide emission analysis wasconsidered, by defining an effective area of cover and coverfamily, for initial and secondary pesticide distributions asderived with the PestLCI Consensus model. Within 1 day ofassessment time after pesticide application, processes oncover leaves, that is, degradation, uptake, and volatilization,

were modeled. The modeling of GCM fills a gap in theconsideration of common farming practices, and demon-strated the potential of GCM to reduce emissions to fieldsoil and related freshwater ecotoxicity impacts. Followingour proposed approach to considering GCM processes, ouremission results reproduce well the effect of GC on pesti-cide distribution as observed in cocoa farm systems byVaikosen et al. (2019), where the reduction in bare soil sur-face with a GC of fallen leaves of the main crop decreasedemission to the top soil. The higher the fraction of effectivearea of cover (composed of living or dead cover), the lowerthe fraction distributed to field soil.

Estimating pesticide interception by the crop or the covercrop is important to estimate pesticide losses to the envi-ronment (Lammoglia et al., 2016). Defining feff,cover with thecover crop occupation fraction and the cover crop canopyfraction according to the growth stage of the cover allowsestimating the pesticide interception by the cover crop,which could reach up to 60% during the installation phase ofthe main crop in our case study. Defining the crop inter-cepted fraction according to the LAI and the pesticidespraying technique for each crop family allows defining theintercepted fraction separately for the crop and the coveralong the whole crop cycle and pesticide applications. As aconsequence, the more precisely the fraction intercepted bythe crop and the cover is defined, the better the estimate ofthe pesticide distribution for a living cover (Figure 3). Theeffective area fraction of cover should preferably be definedby observation. However, LCA practitioners rarely know theGCM and its characteristics. If the studied field cannot be

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TABLE 3 Impact score (IS, in units of PAF m3 d/kgapplied) of the average scenarios with a cover crop for each environmental compartment(continental: rural air, freshwater, natural soil, and agricultural soil), for the two scenarios: tomato and grapevine and %‐change (Equa-tion 17) when not using a cover crop (i.e., bare soil), for the application of two pesticides (mancozeb and pyriproxyfen). Calculation of

%‐change= [(IS_without_cover – IS_with_cover)/IS_with_cover] × 100

Emissiondistribution andcover crop fate Crop IS Rural air IS Freshwater IS Natural soil IS Agricultural Soil Total

Initialdistribution—with coverexportedfromthe field*

Tomato 21 (<1%) 18 (−2%) 10 (1%) 201 (113%) 251 (91%)

Grapevine 26 (<1%) 80 (−1%) 11 (1%) 214 (103%) 331 (66%)

Secondaryemissions—coverexportedfromthe field*

Tomato 21 (<0.1%) 18 (−) 10 (−) 189 (111%) 238 (88%)

Grapevine 26 (<0.1%) 79 () 11 (−) 209 (100%) 326 (64%)

Secondarydistribution—cover buriedin thefield soil**

Tomato 21 (<0.1%) 18 () 10 (−) 414 (−3%) 463 (−3%)

Grapevine 26 (<0.1%) 79 () 11 (−) 424 (−2%) 541 (−1%)

*ffield cover→ and fcover sec‐ were not linked to the impact assessment model used (i.e., USEtox).**fcover sec‐ was assigned to continental agricultural soil in the impact assessment model used (i.e., USEtox).

10 Integr Environ Assess Manag 00, 2021—GENTIL‐SERGENT ET AL.

visited, for crops grown in rows (e.g., orchards, grapevine)an effective area fraction of cover crop of f 0.35eff,cover = canbe assumed for a planted cover at its maximum develop-ment and an effective area fraction of f 0.7eff,cover = for aspontaneous cover crop. Furthermore, a set of commonliving cover crops should be developed according to themain crop characteristics (e.g., on banana production at leafdevelopment stage), defining the cover effective area frac-tion and the cover family.After pesticide application, processes of degradation,

uptake, and volatilization occur on the crop and coverleaves. To estimate the dissipation half‐lives on leaves,which are crop dependent (Fantke et al., 2014), the coverfamily (composed mostly of Pooideae, Panicoideae, orFabaceae) has to be defined. For multispecies cover (e.g.,weed development of multiple species), we recommendselecting the dominant cover crop family as reference.In our two scenarios, the assignment of the emissions on

the cover crop (uptake and left on leaves) to agriculturalsoil (considering the cover crop buried in the field) wascompared with no assignment to any compartment (con-sidering the cover exported from the field). The im-portance of the differences obtained with these twocalculations (IS reduced by 65% for grapevine and by 90%for tomato when cover was not assigned to any environ-mental compartments as emission compared with as-signed to field soil) needs to be relativized compared withthe high uncertainty of the pesticide characterization fac-tors for which uncertainties of 1–3 orders of magnitudeapply (Rosenbaum et al., 2008). Notably, this is caused bythe absence of modeling of the impact on soil life andpesticide metabolites (Notarnicola et al., 2017; van derWerf et al., 2020).In the absence of complete sets of measured emission

fractions to the environment, our proposed approach canonly be qualitatively discussed, notably exploring othermodel design. Most pesticide transfer models at field scale

(e.g., GLEAMS, MACRO, PEARL, PRZM) do not considercover crop, and rather consider GC as plant residues (e.g.,GLEAMS, PRZM; Mottes et al., 2014). Few models considercover crops and associated pesticide transfer (i.e., R‐pest,WATPPASS) as proposed in this study. The R‐pest modelcoupled with SIMBA allows simulating banana and diversecover crops and defining an indicator of pesticide risk ex-posure to surface and groundwater (Tixier et al., 2007; Tixieret al., 2011). The WATPPASS model, a hydrological modelfor small tropical volcanic catchments, allows the consid-eration of various types of ground cover, modifying envi-ronmental characteristics and thus pesticide transfers(Mottes, 2013; Mottes et al., 2015). As in those two models,the consideration of GCM in our proposed approach allowsconsidering an important farming practice and its effects onpesticide transfer to the environment. Nevertheless, theoutputs from those models cannot be compared with ouremission fractions, mainly because they are not based on anemission mass balance as PestLCI Consensus.To generalize our conclusions on GCM pesticide emis-

sions and related impacts, a sensitivity analysis should beperformed, notably testing the GCM modeling with a widerange of pesticides with diverse characteristics (e.g., DT50)and including additional environmental processes, climateconditions, and field crops as well as cover crop types.Furthermore, a wider range of crop types and related ap-plication methods should be tested in line with agronom-ically relevant practices. Overall, our proposal for modelingGCM raises awareness of the fraction of pesticides reachingliving cover inside the field crop, which can affect the dis-tribution of pesticides to the environment with its possibleecotoxicity impacts through agricultural soil emissions.

Future research needs

Additional research efforts are required on the modelingof processes occurring a few days after pesticide application

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pes

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Con�nental Air Con�nental Freshwater Con�nental Natural soil Con�nental Agricultural Soil

FIGURE 4 Contribution to total impact score expressed in PAF m3 d/kg pesticide applied, from each environmental compartment, for the tomato andgrapevine scenarios, considering the presence of cover, using either the initial or secondary distribution, and indicating whether the fraction emitted to cover isassigned to agricultural soil or not (results are mean results obtained with the two pesticides)

GROUND COVER IN PESTICIDE EMISSION MODELING—Integr Environ Assess Manag 00, 2021 11

(secondary distribution), with minimum redundancy with theimpact assessment fate model. Modeling secondary dis-tribution processes requires the accounting of local field,farming practices, soil and climate conditions, which are ofparticular interest to assess farming system diversity. Furtherresearch is required to consistently include additionalprocesses, such as runoff, as well as to account for the in-fluence of GCM on pesticide residues in edible field cropsand on remaining pesticide fractions in unharvested covercrops.More specifically, GC may affect water processes, such as

runoff, leaching, or preferential flow (Alletto et al., 2010;Mottes et al., 2014; Reichenberger et al., 2007); thus, theycan influence the affects. At emission level, GCM shouldreduce emissions to off‐field surfaces and to groundwater(Potter et al., 2007), and consequently, at impact level, totalIS might be reduced for scenarios with a GC. Further re-search is therefore required to incorporate such processesconsistently in our proposed GCM approach, as discussed inGentil et al. (2020b), particularly for tropical crop productionsystems.As recommended in Fantke et al. (2018), pesticide

emissions were assigned to continental rural air, freshwater,agricultural soil, and natural soil compartments of the impactmodel USEtox. Recently, PestLCI initial distributionsand the plant uptake and crop residue exposure modeldynamiCROP (Fantke et al., 2011), which was recentlyadjusted for LCA (Fantke & Jolliet, 2016), were coupled(Gentil et al., 2020a), allowing the consideration of humantoxicity caused by pesticides present in the harvested andconsumed part of field crops. The pesticide deposited onnon‐harvested living or dead plant, inside (e.g., cover crops)or outside the field (e.g., hedge tree), might haveecotoxicity impacts (Sharma et al., 2019) and require furtherresearch to be fully considered. Indeed, if crop residues andunharvested cover crop remain on the field, there could befurther emissions to field soil and air, followed by sub-sequent transfer to freshwater compartments. The modelingof plant root uptake would be required to assess pesticideemissions to crop and cover crop residues (on the non‐harvested stem, roots, and leaves). If an LCA user assumesthat the cover crop has been removed from the field, thenimpacts from pesticides present on the cover need to beassessed according to the subsequent processes applied tothe cover.As for the modeling of pesticide degradation on crop and

cover leaves, volatilization and plant uptake should be re-fined to be modeled as a function of crop family, consid-ering in particular leaf characteristics, which differ acrosscrop types (Fantke & Juraske, 2013; Fantke et al., 2014).Further improvement of GCM should be considered for

modeling dead GC. Indeed, mulch or crop residues left onfields (e.g., stripping of banana while growing) are usedlargely in tropical conditions to keep moisture or reduce soilerosion (Lewis et al., 2016). At the initial distribution stage,all types of GC (living or dead mulch) can be modeled, bydefining the effective area fraction of cover (f .eff,cover) At the

secondary distribution stage, specificities of the dead covermust be considered and modeled. Mulch is generallymodeled as the first soil layer (Mottes et al., 2015) with itsown characteristics notably of composition and degradationrate (Cassigneul et al., 2015). Plastic mulches are also usedon vegetable production, or on pineapple production forexample, generating specific water flows and consequentlyrequiring specific modeling (Dusek et al., 2010). The in-clusion of dead covers would be an important future stepforward for modeling different types of GCM in the esti-mation of pesticide emissions in LCA and elsewhere.

Overall, the modeling of GC opened the path to themodeling of pesticide emissions in intercropping systems.This is particularly important because these systems arewidely conducted in market gardening in tropical conditionsto ensure income stability (Malézieux et al., 2009), increasecrop yield per hectare, and optimize field conditions forgrowing certain crops (e.g., banana and cocoa).

CONCLUSIONSThe inclusion of GCM in pesticide emission modeling was

proposed, defining an effective area of cover and the coverfamily through initial and secondary pesticide distributions.Across the two scenarios on tomato production in Marti-nique and on grapevine cultivation in the Loire Valley, themodeling of GCM allowed highlighting the potential of soilcover to reduce pesticide emissions and related freshwaterecotoxicity impacts. Including a new fraction on and in covercrop leaves, the emissions to field soil decreased by a factorof 3. During the secondary distribution, over the threeprocesses occurring on the cover leaves, the fraction takenup by the cover leaves was predominant with more than99% contribution to these processes. At both initial andsecondary distribution levels, considering the cover ex-ported from the field and consequently not assigned to anyenvironmental emission compartment, total IS were reducedby approximately 65% and 90% compared with bare soil, forgrapevine and tomato, respectively. Additional processes,such as runoff, should be considered in future efforts, alongwith accounting for the influence of GCM on pesticide res-idues in edible field crops and on remaining pesticidefractions in unharvested cover crops. Indeed, if crop resi-dues and unharvested cover crop remain on the field, therecould be further emissions to soil and air. The inclusion ofdead cover would be an important step toward achievingthe modeling of various types of GCM.

From the initial work of Renaud‐Gentié et al. (2015) onvines, the consideration of GCM as common farming prac-tice opened the possibility of modeling it more widely for allcrops with living cover crop. The modeling of living covercrop also opened the path toward the modeling of pesticideemissions in intercropping systems, widely conducted inmarket gardening, particularly in tropical regions. With that,our proposed approach constitutes a valuable starting pointfor addressing GCM practices in emission and impact as-sessments applied in LCA and elsewhere.

Integr Environ Assess Manag 2021:1–15 © 2021 The Authorswileyonlinelibrary.com/journal/ieam

12 Integr Environ Assess Manag 00, 2021—GENTIL‐SERGENT ET AL.

ACKNOWLEDGMENTThis work was financially supported by ADEMEMartinique

through the InnovACV project (No. 17MAC0038), CIRAD viathe Rivage project funded by Martinique's European Re-gional Development Fund (MQ0003772‐CIRAD), ADEMEFrance via the OLCA‐Pest project (grant agreement No. 17‐03‐C0025), the SPRINT Project (Grant Agreement 862568)funded by the European Commission through Horizon 2020,and the FNS‐Cloud Project (Grant Agreement 863059)funded by the European Commission through Horizon 2020.We thank Thomas Nemecek, Philippe Roux, AssumpcióAntón, Nancy Peña, and Pierre Naviaux from the OLCA‐Pestproject team for their feedback on an earlier version of themanuscript.

CONFLICT OF INTERESTThe authors declare no conflicts of interest.

DATA AVAILABILITY STATEMENTThe model is available at https://pestlciweb.man.dtu.dk/.

Further information is available upon request from corre-sponding author Peter Fanke (pefan@dtu.dk).

SUPPORTING INFORMATIONThe supporting information file contains information and

characteristics of the pesticides used in the case study.

ORCIDCéline Gentil‐Sergent https://orcid.org/0000-0001-7301-1243Carlos Melero https://orcid.org/0000-0001-7771-6419Peter Fantke https://orcid.org/0000-0001-7148-6982

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